(2024) 22:309
Taeubert et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03529-2

BMC Medicine

RESEARCH

Open Access

Adults prenatally exposed to the Dutch
Famine exhibit a metabolic signature associatedwith a broad spectrum of common diseases
M. Jazmin Taeubert1, Thomas B. Kuipers1, Jiayi Zhou2, Chihua Li3,4,5, Shuang Wang6, Tian Wang6, Elmar W. Tobi1,
BBMRI-NL Metabolomics consortium, Daniel W. Belsky2,3, L. H. Lumey1,3 and Bastiaan T. Heijmans1*

Abstract
Background Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease,including obesity and type 2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profilingto identify the metabolic changes that are associated with survival of prenatal famine exposure during the Dutch
Famine at the end of World War II and subsequently assess their link to disease.
Methods NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine(mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine
exposure with those of 154 common diseases.
Results Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2–0.3 SD, p < 3 × ­10−3). The metabolic biomarker
signature of prenatal famine exposure was positively correlated to that of incident type 2 diabetes from the UK
Biobank (r = 0.77, p = 3 × ­10−27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1 × ­10−18). Remarkably, this association extended to 115 common diseases for which
signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2 × ­10−4). Correlations among metabolic signatures of famine exposureand disease outcomes were attenuated when the famine signature was adjusted for body mass index.
Conclusions Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvementof obesity.
Keywords Cardiovascular disease risk, Metabolomics, Type 2 diabetes, Prenatal adversity, Famine, Disease association

Membership of the BBMRI-NL Metabolomics consortium is provided in
Additional file 1.
*Correspondence:
Bastiaan T. Heijmans
b.t.heijmans@lumc.nl
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Taeubert et al. BMC Medicine(2024) 22:309

Background
Metabolomics is a powerful tool for illuminating molecular phenotypes underpinning disease [1]. With nuclear
magnetic resonance (NMR) approaches, metabolomics
is now possible within large-scale epidemiologic studies
and biobanks. Within these settings, NMR metabolomics
has identified metabolic changes across a broad spectrumof diseases, revealing both common and disease-specificmetabolic biomarkers [2–5]. Less is known about how
the metabolome may reflect or mediate effects of prenatal exposure on disease pathogenesis. Here, we investigate the long-term metabolomic sequelae associated withgestational exposure to famine, an established risk factor
for the development of metabolic disease [6, 7].
The Dutch Hunger Winter of 1944–1945, a 6-monthfamine at the end of World War II, provides a unique setting to study the long-term effects of an adverse prenatal environment [6, 8, 9]. Previous studies revealed thatprenatal famine exposure is associated with an increased
risk in unfavorable metabolic phenotypes in adulthoodincluding increased fasting glucose and triglyceride levels, obesity, and type 2 diabetes [10–17]. These associations have also been observed for other historical famines[6]. To date, a comprehensive view of metabolic changes
linked to prenatal famine exposure is lacking. Therefore,
in this study, we seek to define the metabolomic differences associated with prenatal famine exposure andhow these metabolic biomarkers are linked to diseases.
We expect to identify changes in metabolic biomarkers
that reflect metabolic diseases known to be associatedwith prenatal famine exposure, while the metabolomics
approach also provides the opportunity to uncovermetabolic biomarkers related to diseases not previously
reported in the context of prenatal famine exposure.
We profiled samples for 944 participants from theDutch Hunger Winter Families Study using nuclear
magnetic resonance (NMR) metabolomics. First, we
compared individuals exposed to prenatal faminewith unexposed control participants on 168 different
serum metabolic biomarkers to define the metabolic
differences associated with prenatal famine exposure.
To exclude that effects were partially driven by genetic
rather than environmental influences, we corrected forgenetic effects using polygenic risk scores that are known
to explain part of the variance in metabolic biomarker
levels [18, 19]. Second, we compared the metabolomewide signature of prenatal famine exposure to an atlas ofmetabolic signatures marking risk of a range of common
diseases to test for commonalities between the metabolic
differences associated with prenatal exposure to famine
and those previously associated with future disease risk.
Our study reveals specific metabolic biomarker alterations and broader connections with a range of chronic

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diseases, providing new insights into how prenatal famine exposure shapes health across the life course.

Methods
Study population

The Dutch Hunger Winter Families study (DHWFS) is
described in detail elsewhere [20]. In short, historicalbirth records were retrieved from three institutions infamine-exposed cities of all singleton births between 1
February 1945 and 31 March 1946 and a systematic sample of births born in 1943 or 1947. From these records,
we identified 3307 individuals whose mothers were
exposed to the famine during or immediately precedingthat pregnancy and unexposed time-controls born before
or after the famine. In total, 2300 of the 3307 individuals were located through population registries and wereinvited to participate in a telephone interview and in aclinical examination, together with a same-sex sibling
not exposed to the famine (family-control). No differences were found in birth characteristics when comparing the subjects who were located to those who could not
be located or when comparing the positive responders to
those who did not respond to our invitation letter [20].
We conducted 1031 interviews and 971 clinical examinations between 2003 and 2005. One non-biological sibling identified with genetic analyses was excluded fromthe cohort. NMR metabolomics profiling was performedon serum samples of 962 individuals. Our sample for thisstudy included 944 individuals after excluding non-fasted
samples (n = 17) and an outlier in the metabolomics
dataset as identified with principal component analysis(n = 1). For more information, refer to the flow chart ofour study population (Additional file 1: Fig. S1, Additional file 1: Table S1).
The Dutch Hunger Winter Families study was approvedby the Medical Ethics Committee of Leiden University
Medical Center (P02.082), and the participants provided
verbal consent at the start of the telephone interview
and written informed consent at the start of the clinicalexamination.
Famine exposure definitions

Food rations were distributed centrally and below
900 kcal/day between 26 November 1944, and 15 May
1945 [8]. We defined famine exposure by the number ofweeks during which the mother was exposed to < 900 kcal/
day after the last menstrual period (LMP) recorded on thebirth record [20]. The gestational period was estimatedfrom the LMP of the mother and the date of birth of thechild. To analyse the effect of timing of gestational exposure, we subdivide the gestational period into units of
10 weeks. We considered the mother exposed in gestational weeks 1–10, 11–20, 21–30, or 31 to delivery if theseTaeubert et al. BMC Medicine(2024) 22:309

gestational time windows were entirely contained within
this period and had an average exposure of < 900 kcal/day
during an entire gestation period of 10 weeks. As the famine lasted 6 months, some participants were exposed tofamine during two adjacent 10-week periods. In chronological order, pregnancies with LMP between 30 April
1944 and 24 August 1944 were considered exposed inweeks 31 to delivery, between 9 July 1944 and 15 October
1944 in pregnancy weeks 21–30, between 17 September
1944 and 24 December 1944 in pregnancy weeks 11–20,
and between 26 November 1944 and 4 March 1945 in
pregnancy weeks 1–10. Individuals with a LMP between
4 February and 12 May 1945 which were exposed to an
average of < 900 kcal/day for less than 10 weeks before
conception and up to 8 weeks post-conception are
denoted as the weeks 9–0 weeks group. We defined individuals exposed to one or two of these definitions exposedto ‘any’ gestational exposure.
Characteristics

Information on health history, including informationon the use of cholesterol-lowering drugs, was collected
through telephone interviews. Measurement of heightwas carried out to the nearest millimeter using a portablestadiometer (Seca), and body weight was measured to thenearest 100 g by a portable scale (Seca). BMI was calculated from these measures (weight (kg)/[height (m)]2).
Waist circumference (at level of iliac crests, intersection
with midaxillary line) was measured to the nearest 1 mmwith the use of a non-extensible measuring tape (Hoechstmass). Cholesterol measures were reported previously[14] and were assessed using standard enzymatic assays.
LDL cholesterol was calculated for individuals with a triglyceride concentration lower than 400 mg/dl using theFriedewald formula. A blood draw was performed at thestart of a 75-g oral glucose test, and fasted glucose wasquickly assessed in serum by hexokinase reaction on a
Modular P800 (Roche). The presence of type 2 diabetes
was either determined through previous health history or
defined as fasting glucose ≥ 7.0 mmol/l or 2 h glucose tolerance test ≥ 11.1 mmol/l [21].
Metabolic biomarker quantification

Metabolic biomarkers were measured from serum samples using a high-throughput 1H-NMR metabolomics
platform developed by Nightingale Health Ltd. (Helsinki,
Finland; nightingalehealth.com; biomarker quantification
version 2021). Details of the procedure and applicationof the NMR metabolomics platform have been described
elsewhere [22, 23]. This method provides simultaneousquantification of 249 metabolic biomarkers, of which
168 are directly measured and 81 are derived metabolic
biomarkers, including 37 clinically validated metabolic

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biomarkers certified for diagnostic use. The metabolic
biomarkers measured include amino acids, ketone bodies, lipids, fatty acids, and lipoprotein subclass distribution, particle size, and composition. A subset of thebiomarkers was selected for inclusion in the presented
analysis, focusing on the 168 directly measured metabolic biomarkers.
Values below the detection limit were treated as missing and imputed by setting them to the minimum value
for each metabolic measure. A value of one was added to
all metabolic biomarkers containing zeroes (i.e., x + 1),
which indicated that they were below the limit of quantification. All metabolic biomarkers were then natural
logarithmic transformed to obtain an approximately normal distribution. The metabolic biomarkers were subsequently scaled to standard deviation (SD) units (mean 0,SD 1) for use in the analysis, enabling the comparison ofmultiple metabolic measures with different units or withlarge differences in their concentration distributions.
Genotype data generation and polygenic scores

From our metabolomics sample population, 931 individuals also had genotype data available. Genotype data
were measured using the Illumina Infinium™ Global
Screening Array (GSA) genotyping platform (version
24 v3.0. Illumina Inc., San Diego, USA) by the Human
Genomics Facility in the Genetic Laboratory Rotterdam(Rotterdam, the Netherlands). Imputation was performed
using the 1000G P3v5 reference panel [24]. Polygenic
scores were calculated for tyrosine, leucine, and glucose
levels with the PRSice-2 software using the independent
hits of publicly available genome-wide association study(GWAS) summary statistics (Additional file 1: Table S2)[25]. The base GWAS study utilized the same NMR platform and had participants with the same ancestry (European) as those in our study [18]. The polygenic scores
were residualized on the first ten genetic principal components and subsequently scaled to standard deviation(SD) units (mean 0, SD 1) for analysis.
Statistical analysis

All analyses were performed in the R programming
environment (R version 4.2.2). For all linear regression
analyses, we used linear regression within a generalized
estimating equations framework to account for the correlation between sibships (R geepack package, version
1.3.9) [26].
Validation of NMR metabolomics measures

We first validated the NMR measurements by testing
the correlation between glucose, triglycerides, total cholesterol, LDL, and HDL cholesterol measured by routine clinical chemistry and Nightingale Health NMR

Taeubert et al. BMC Medicine(2024) 22:309(Additional file 1: Fig. S2) (26, 27). We subsequently
tested whether previously observed associations between
prenatal famine exposure and these five metabolic biomarkers as measured by routine clinical chemistry wereconsistently found when the same biomarkers were
measured using Nightingale Health NMR. For this, weused linear regression models adjusted for age, sex, andcholesterol-lowering medication.
Metabolome‑wide association study (MWAS)

Next, we performed a metabolome-wide association
study of prenatal famine exposure by assessing the relationship between famine exposure and 168 metabolic
biomarkers through linear regression models adjusted
for age, sex, and cholesterol-lowering medication (mainmodel). Due to the correlated nature of the metabolic
biomarkers, 95% of the variation in the 168 metabolic
biomarkers was explained by 14 principal components(Additional file 1: Fig. S3). Therefore, as previously
described [27–29], we corrected for 14 independent
tests using Bonferroni multiple testing correction (pvalue = 0.05/14 = 3.57 × ­10−3).
Sensitivity analysis of the MWAS results

Sensitivity analyses were performed to assess the robustness of the results of the metabolome-wide association
study of prenatal famine. First, to assess the effect offamine exposure independent of anthropometric measures or type 2 diabetes, the main model was additionally adjusted for BMI, waist circumference, and type 2
diabetes. Second, to isolate environmental influenceson our metabolic outcomes, we corrected for potential
differences in metabolic biomarker levels that could be
attributed to genetics by additionally adjusting the mainmodel for the polygenic scores of the metabolic biomarkers. Third, to check for potential differences between
sexes, sex-stratified analyses were performed adjustingfor the same covariates as the main model and an interaction term for sex and metabolic marker was includedin the model to test whether potential differences were
statistically significant. Fourth, potential gestation timing specific effects of famine exposure were examinedby subdividing famine exposure into 5 gestational time
windows. In the regression analysis, the single indicatorof famine exposure was replaced with indicator variables
identifying exposure within each of the gestational time
windows.
Comparison to metabolic biomarker signatures of diseases

To assess whether the metabolic biomarkers associatedwith prenatal famine exposure were linked to metabolicchanges previously associated with future disease risk,
we first compared the metabolic biomarker signature

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associated with prenatal famine exposure to the metabolic biomarker signature predicting the incident risk oftype 2 diabetes. For this, we utilized published resultsfrom a metabolome-wide study on incident type 2 diabetes using UK Biobank data [2]. Specifically, we correlated
the effect sizes of famine exposure in DHWFS with theeffect sizes in the UK Biobank for incident type 2 diabetes across the 135 shared metabolic biomarkers in bothdatasets.
We then extended our analysis and correlated the metabolic profile of prenatal famine to the publicly available
metabolic signatures of a large set of diseases as estimated with UK Biobank data [3]. Out of 674 incident
diseases available in the metabolomics atlas, we selected
those with at least 1000 cases (out of a total population
ranging from ~ 103,300 to ~ 118,000) to represent common diseases (n = 162), and those with at least one significant association with a metabolic biomarker in the UK
Biobank analysis (p < 5 × ­10−4) (n = 154), resulting in 154
diseases. The effect sizes of the overlapping 168 metabolic
biomarkers were correlated between prenatal famine andeach disease. We corrected for multiple testing using Bonferroni correction (p value = 0.05/154 = 3.2 × ­10−4). Finally,
we re-estimated the effect sizes of the association between
prenatal famine exposure and all 168 metabolic biomarkers,
while additionally adjusting for BMI and waist circumference. We then repeated the correlation analysis comparing
this BMI- or waist circumference-adjusted prenatal famine
metabolic biomarker signature with the 154 metabolic biomarker disease signatures from the UK Biobank.

Results
Population characteristics

Within the Dutch Hunger Winter Families Study, fasting NMR metabolomics data were available for 944
study participants. Among these participants, 480 (51%)
were prenatally exposed to famine, and 464 (49%) were
controls (including unexposed time controls born at thesame institution as the exposed individuals and unexposed same-sex sibling controls both born either before
or conceived after the famine). As previously reported,famine-exposed participants had an increased BMI [11]and a higher prevalence of type 2 diabetes [16], and controls were on average 0.9 years younger than famineexposed. No differences were observed in sex or the useof cholesterol lowering medication (Table 1).
Validation of NMR metabolomics measures

To confirm the validity of the newly measured metabolomics measures in our study, we first compared our
results to previously measured clinical chemistry data
available for five metabolic biomarkers, namely fasted
glucose, triglycerides, total cholesterol, LDL, and HDL

Taeubert et al. BMC Medicine(2024) 22:309

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Table 1 Population characteristics
Controls(n = 464)

Famine-exposed p value(n = 480)

Age, years (SD)57.9 (5.4)

58.8 (0.5)

1.1 × ­10−3

Sex, males, n (%)

200 (43.1)

225 (46.9)

0.24

Use of cholesterol-lowering
medication, n (%)

55 (11.9)

61 (12.7)

0.69

Body mass index, kg/m2 (SD) 27.0 (4.2)

28.2 (4.8)

1.6 × ­10−4

Type 2 diabetes, n (%)

61 (12.8)

0.02

38 (8.2)

Values are means (standard deviation) or numbers of subjects (valid %) shownfor famine-exposed and controls of the study population. Comparing the twocategories by a two-sample t-test or chi-square test, as appropriate

cholesterol [14, 17]. We observed high correlations(r ≥ 0.9) in line with previous studies [3, 30], and the associations of clinical chemistry and NMR biomarkers withprenatal famine were consistent (Additional file 1: Fig. S2,
Additional file 1: Table S3).

Metabolome‑wide association study on prenatal famine
exposure

Next, we examined the association of any prenatal famine exposure with all 168 metabolic biomarkers individually. Prenatal famine exposure was associated with higher
tyrosine (effect size 0.28 SD), leucine (0.21 SD), glucose (0.23 SD), and isoleucine (0.18 SD) concentrations(p < 3 × ­10−3; all analyses adjusted for age, sex, and use ofcholesterol-lowering drugs) (Fig. 1A, Additional file 2:
Table S4).
We performed three sets of follow-up analyses fortyrosine, leucine, and glucose to gain further insight
into these associations. Isoleucine was excluded because
it was highly correlated with leucine (r = 0.9), both arebranched-chain amino acids, while leucine showed thestronger association with famine exposure (Fig. 1B, Additional file 1: Fig. S4). First, the associations between prenatal famine exposure and tyrosine, leucine, and glucose

Fig. 1 Metabolome-wide association study on prenatal famine exposure. A Association of prenatal famine exposure with 168 metabolic
biomarkers. Regression models were adjusted for age, sex, and cholesterol-lowering medication and correlation within sibships were controlledfor (main model). Scattered points represent metabolic biomarkers: the x-axis shows the effect size for the association of famine with the respective
metabolic biomarker, while the y-axis is negative log of the p value. The grey line represents the significance threshold for this analysis (pvalue = 3.57 × ­10−3). B Heatmap showing the correlation of famine-associated metabolic biomarkers. Pearson’s correlation was calculated for eachmetabolic biomarker pair. C Sensitivity analyses on famine-associated metabolic biomarkers. Main: main model; BMI-adjusted: main model
additionally adjusting for BMI; Diabetes-adjusted: main model additionally adjusted for type 2 diabetes; polygenic score (PGS)-adjusted: main model
additionally adjusted for the polygenic score of the metabolic biomarkers. Effect estimates and 95% confidence intervals are depicted for eachmodel and are reported in standard-deviation (SD) units of the log-transformed metabolic biomarkers

Taeubert et al. BMC Medicine(2024) 22:309

remained after including BMI, waist circumference, ortype 2 diabetes as a covariate in the model (Fig. 1C, Additional file 1: Table S5). Second, we adjusted these associations for potential differences that may be attributed
to genetics. Since the polygenic scores of each metabolic
biomarker explained only approximately 1–5% of theirvariance (Additional file 1: Table S2), the associations
were not affected by including the polygenic scores ascovariates (Fig. 1C). Third, we explored whether associations between famine exposure and the three metabolic biomarkers were dependent on sex or the timing of
exposure during gestation (Additional file 1: Fig. S5). We
found no evidence that our findings differed according tosex or timing of exposure during pregnancy. The effectsizes across different exposure timing subgroups weresimilar to estimates of the main analysis, although the
majority of these exposure timing sub-categories were
not statistically significant (0.02 ≤ p ≤ 0.8). In the sexstratified analysis, the effect sizes for glucose and tyrosine
were lower in females than males (0.0002 ≤ p ≤ 0.08), but
we found no statistical evidence for effect modification(interaction p values > 0.39).
Comparison to metabolic biomarker signatures of diseases

The metabolic biomarkers we found to be associatedwith prenatal famine exposure, namely tyrosine, glucose, leucine, and isoleucine, were previously reported
to be associated with type 2 diabetes risk [2, 31]. To
further investigate whether these associations reflect
an increased risk of type 2 diabetes among individuals

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prenatally exposed to famine, we utilized a previously
reported metabolic biomarker signature of incident type2 diabetes from UK Biobank [2] and compared it to thecomplete set of biomarker associations in our study. Specifically, we took the effect sizes of 135 metabolic biomarkers for the risk of type 2 diabetes and comparedthem to the effect sizes we observed for prenatal famine.
The metabolic biomarker signature of prenatal famine
exposure was highly correlated with that of incident type2 diabetes (r = 0.77, p = 3 × ­10−27; Fig. 2A). This similarity
persisted when we re-estimated the effect sizes for prenatal famine exposure after excluding participants with
type 2 diabetes (r = 0.67, p = 1 × ­10−18) (Fig. 2B).
To explore whether the metabolic biomarker profile
of prenatal famine exposure may reflect a risk of diseases beyond type 2 diabetes, we extended the analysis to recently published atlas of signatures of a wide
range of diseases in the UK Biobank [3]. We focused on
metabolic biomarker signatures for the future onset of
disease obtained from individuals not affected by thedisease of interest at baseline. For the analysis, we utilized a subset of 154 common incident diseases that
had at least one metabolic biomarker association in
the UK Biobank (p < 5 × ­10−4). Remarkably, the metabolic biomarker signature of prenatal famine exposure was positively correlated with the metabolic
biomarker signature of 115 diseases (75%; 0.3 ≤ r ≤ 0.9,
p < 3.2 × ­10−4) and negatively correlated with 13 diseases(8%; − 0.9 ≤ r ≤ -0.4, p < 3.2 × ­10−4) (Additional file 2:
Table S6). The metabolic biomarker signature of prenatal

Fig. 2 Correlation analysis of the metabolic biomarker signature associated with famine and the metabolic biomarker signature associatedwith incident type 2 diabetes. A Overall metabolic biomarker signature comparison of 135 metabolic biomarkers for prenatal famine exposure
and incident type 2 diabetes (r = 0.77, p = 3 × ­10−27) as established in the UK Biobank Study in a 12-year follow-up. B Overall metabolic biomarker
signature comparison of 135 metabolic biomarkers for prenatal famine exposure (excluding 99 individuals with type 2 diabetes from the analysis)
and incident type 2 diabetes as established in the UK Biobank Study in a 12-year follow-up (r = 0.67, p = 1 × ­10−18). The effect size estimates for eachmetabolic biomarker are shown as points. Famine-associated metabolic biomarkers are indicated in blue

Taeubert et al. BMC Medicine(2024) 22:309

famine exposure exhibited the strongest correlation withthe signature of the future risk of myocardial infarction(r = 0.9, p = 1.8 × ­10−47). Other diseases with a strong correlation (r ≥ 0.7) included those related to the digestivesystem, diseases with an endocrine, and nutritional andmetabolic component as well as diseases of the nervous
system (Fig. 3). The diseases displaying a negative correlation were primarily associated with injury and otherconsequences of external causes, such as fractures andopen wounds (Additional file 2: Table S6).
We hypothesized that a potential common factor
among the diseases with a similar metabolic biomarker
signature is obesity. To test this hypothesis, we re-estimated the effect sizes for prenatal famine exposure while
additionally adjusting for BMI and then re-calculated thePage 7 of 10

correlation between the resulting BMI-adjusted metabolic biomarker signature with the signatures of the 154
common incident diseases (Additional file 2: Table S6).
The strength of the correlations was consistently attenuated across all diseases (mean =  − 58%; SD = 19%).
Among the 30 diseases whose metabolic biomarker signature was most similar to that of prenatal famine, theattenuation ranged between 27 and 48%, and the correlations remained moderate (0.4 ≤ r ≤ 0.6). The degree ofattenuation was not linked to whether the disease had
an obvious metabolic component (Fig. 4). Similar results
were obtained when repeating the analysis adjusting for
waist circumference instead of BMI (Additional file 1:
Fig. S6).

Fig. 3 Correlation analysis of the metabolic biomarker signature associated with famine and the metabolic biomarker signature associatedwith various common diseases. Heatmap showing the effect size estimates of the 30 most correlated diseases to prenatal famine exposure. The
columns are clustered according to the metabolic biomarker effect sizes and the rows are ordered according to the correlation of the metabolic
biomarker signature of the disease to prenatal famine exposure (Pearson r for IK21 Acute myocardial infarction = 0.85, Pearson r for E11 type 2
diabetes mellitus = 0.69). Only metabolic biomarkers that are nominally associated with prenatal famine exposure are shown (p < 0.05). The diseases
are shown with their ICD-10 (International Classification of Diseases 10th Revision) classification. The full names of the metabolic biomarkers can befound in Additional file 2: Table S4

Taeubert et al. BMC Medicine(2024) 22:309

Discussion
We further defined the metabolic phenotype associatedwith prenatal famine exposure using nuclear magnetic
resonance (NMR) metabolomic profiling. We show thatprenatal exposure to famine is associated with specificmetabolic differences later in life, including higher levels of branched-chain amino acids (BCAA), an aromatic
amino acid, and glucose. In addition, we report that the
metabolic biomarker signature of prenatal famine has
marked similarities to the signature of a wide range ofcommon diseases.
Our study indicated specific differences in the metabolic profiles of famine-exposed individuals compared
to controls. We observed that those exposed to famine prenatally have higher levels of the aromatic aminoacid tyrosine, the branched-chain amino acids leucine
and isoleucine, and glucose six decades after exposure. All four metabolic biomarkers have been linked totype 2 diabetes and thus support the known association
between prenatal famine exposure and type 2 diabetes
risk in adulthood [2, 6]. In addition, higher levels of otherbranched-chain and aromatic amino acids such as valine

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and phenylalanine that have also been associated with
type 2 diabetes showed a nominally significant association with prenatal famine exposure, further supporting
the link between prenatal famine and type 2 diabetes risk[2]. Interestingly, the metabolic biomarker associations
with prenatal famine were independent of BMI and type2 diabetes status, indicating that they may not be fully
driven by the higher BMI and increased prevalence oftype 2 diabetes among famine-exposed individuals.
The link with a higher type 2 diabetes risk among individuals exposed to famine in the prenatal period was
supported by investigating the complete range of metabolic biomarkers. We observed a strong resemblance inthe metabolic biomarker signature of prenatal faminewith that of the future onset of type 2 diabetes [2]. Moreover, the strong correlation of the famine signature with
the incident type 2 diabetes signature persisted after
excluding participants who were already diagnosed with
type 2 diabetes at the time of assessment. This result
reinforces that type 2 diabetes is a main health outcomeof prenatal famine exposure [6] and indicates that evenexposed individuals not diagnosed with type 2 diabetes

Fig. 4 Effect of additional adjustment of BMI in the correlation analysis of the metabolic biomarker signature associated with famine
and the metabolic biomarker signature associated with various common diseases. The main model within the DHWFS cohort was adjusted for age,sex, and cholesterol-lowering medication. The BMI-adjusted model within the DHWFS cohort was adjusted for age, sex, cholesterol-lowering
medication, and BMI. The effect sizes estimated for these two models of prenatal famine exposure were each correlated to the effect sizes estimatedfor the risk of common diseases. The 30 diseases most correlated to the metabolic biomarker signature of prenatal famine exposure are shownTaeubert et al. BMC Medicine(2024) 22:309

have an increased risk of developing this condition in the
future.
Upon extending our analysis beyond type 2 diabetes, we observed a striking similarity between the metabolic biomarker signature of famine exposure and a wide
range of other incident disease signatures. This not only
included conditions like disorders of lipoprotein metabolism, obesity, and type 2 diabetes but also a priori lessexpected diseases like osteoarthritis, kidney stones, anddepressive disorders. Interestingly, these high correlations were substantially attenuated for all incident diseases
when we repeated the analysis using a metabolic biomarker
profile of prenatal famine that was adjusted for BMI. Ourfindings suggest that BMI is a shared risk factor for or consequence of the diseases and that the metabolic biomarkers
measured by the NMR platform used may have a particularly strong association with BMI. Of note, after accounting for BMI, moderate correlations between the metabolic
biomarker profiles of incident disease and prenatal famine
remained. Our findings combined with previous studies
highlight that the NMR platform is especially useful for disease risk prediction, but is of limited value to gain mechanistic insights in specific diseases. Further studies withmore comprehensive metabolomics platforms are needed
to fully understand why the metabolic biomarker signatureof prenatal famine exposure links to a broad range of diseases, including effects independent of obesity.

Conclusions
Prenatal exposure to famine is associated with marked
metabolic alterations later in life. Differences in individual metabolic biomarkers include higher levels of
branched-chain amino acids, aromatic amino acids, andglucose. Moreover, the metabolic biomarker signature
characteristic of prenatal famine strongly resembles thatof a diverse set of diseases. Overall, our findings underscore the broad impact of prenatal famine on adult healthand highlight obesity as a plausible contributing factor.
Abbreviations
BCAA​	Branched-chain amino acids
BMI	Body mass index
DHWFS	Dutch Hunger Winter Families study
GWAS	Genome-wide association study
LMP	Last menstrual period
NMR	Nuclear magnetic resonanceSD	Standard deviation

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03529-2.
Additional file 1: Table S1. Comparison of participants’ characteristics at
different points of the study. Table S2. Polygenic scores of metabolic biomarkers associated with prenatal famine exposure. Table S3. Association

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of prenatal famine exposure with five metabolic biomarkers, as measured
by NMR and clinical chemistry. Table S5. Additional adjustment of mainmodel* for BMI and waist circumference for prenatal famine-associatedmetabolic biomarkers. Fig. S1. Flow chart of the study population. Fig. S2.
Scatter plots and correlations between metabolic biomarkers measured
by both NMR and clinical chemistry. Fig. S3. Scree plot of the Principal
Component analysis (PCA) of the 168 metabolic markers included in our
analysis. Fig. S4. Clustering of the metabolic biomarkers according to theircorrelation. Fig. S5. Sex- and exposure timing-specific analyses on famineassociated metabolic biomarkers. Fig. S6. Effect of additional adjustmentof BMI and waist circumference in the correlation analysis of the metabolic
biomarker signature associated with famine and the metabolic biomarker
signature associated with various common diseases. Supplemental information. BBMRI-NL Metabolomics consortium banner.
Additional file 2: Table S4. Association of prenatal famine exposure with
168 metabolic biomarkers. Table S6. Correlation of the metabolic biomarker signature of prenatal famine exposure to the metabolic biomarker
signature of diseases.
Acknowledgements
Not applicable.
Authors’ contributions
M.J.T., D.W.B., L.H.L. and B.T.H. were involved in the conception, design, andconduct of the study and interpretation of the results. M.J.T. performed theanalyses and wrote the first draft of the manuscript. T.B.K., J.Z., C.L., S.W., T.W.,
E.W.T., D.W.B, L.H.L, and B.T.H. critically reviewed the manuscript. All authors
approved the final version of the manuscript.
Authors’ X handles
X handles: @bds_lumc (Biomedical Data Science Department at Leiden University Medical Center), @LUMC_Leiden (Leiden University Medical Center).
Funding
This research was supported by US NIH grants (R01HL67914, R01AG066887,
and R01AG042190). The NMR metabolomics measurement of the DHWFS
samples was funded by the BBMRI-NL Metabolomics consortium (a research
infrastructure financed by the Dutch government, NWO 184.021.007 and
184.033.111). DWB is a fellow of the Canadian Institute for Advanced ResearchChild Brain Development (CIFAR CBD) Network. The funders had no role instudy design, data collection and analysis, decision to publish, or preparationof the manuscript.
Availability of data and materials
Data is provided within the manuscript or supplementary information files.

Declarations
Ethics approval and consent to participate
The Dutch Hunger Winter Families study was approved by the Medical Ethics
Committee of Leiden University Medical Center (P02.082), and the participants
provided verbal consent and written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Biomedical Data Sciences, Leiden University Medical Center,
Leiden, The Netherlands. 2 Butler Columbia Aging Center, Columbia University,
New York, NY, USA. 3 Department of Epidemiology, Mailman School of Public
Health, Columbia University, New York, NY, USA. 4 Survey Research Center,
Institute for Social Research, University of Michigan, Ann Arbor, MI, USA. 5 StateKey Laboratory of Quality Research in Chinese Medicine, University of Macau,
Macao SAR, China. 6 Department of Biostatistics, Mailman School of Public
Health, Columbia University, New York, NY, USA.

Taeubert et al. BMC Medicine(2024) 22:309

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Received: 15 March 2024 Accepted: 12 July 2024

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